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Dynamic models in space and time

  • Elhorst, J.P.

    (Groningen University)

This paper presents a first-order autoregressive distributed lag model in both space and time. It is shown that this model encompasses a wide series of simpler models frequently used in the analysis of space-time data as well as models that better fit the data and have never been used before. A framework is developed to determine which model is the most likely candidate to study space-time data. As an application, the relationship between the labor force participation rate and the unemployment rate is estimated using regional data of Germany, France and the UK derived from Eurostat, 1983-1993.

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File URL: http://irs.ub.rug.nl/ppn/240533380
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Paper provided by University of Groningen, Research Institute SOM (Systems, Organisations and Management) in its series Research Report with number 00C16.

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Date of creation: 2000
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Handle: RePEc:dgr:rugsom:00c16
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  1. Breusch, Trevor S & Wickens, Michael R., 1987. "Dynamic Specification, the Long Run and the Estimation of Transformed Regression Models," CEPR Discussion Papers 154, C.E.P.R. Discussion Papers.
  2. Mizon, Grayham E., 1995. "A simple message for autocorrelation correctors: Don't," Journal of Econometrics, Elsevier, vol. 69(1), pages 267-288, September.
  3. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  4. Florax, Raymond & Folmer, Henk, 1992. "Specification and estimation of spatial linear regression models : Monte Carlo evaluation of pre-test estimators," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 405-432, September.
  5. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
  6. Clapp, John M & Rodriguez, Mauricio, 1999. "Erratum: Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 19(1), pages 85, July.
  7. Anselin, Luc & Hudak, Sheri, 1992. "Spatial econometrics in practice : A review of software options," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 509-536, September.
  8. P Burridge, 1981. "Testing for a common factor in a spatial autoregression model," Environment and Planning A, Pion Ltd, London, vol. 13(7), pages 795-800, July.
  9. Gilbert, Christopher L, 1986. "Professor Hendry's Econometric Methodology," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 283-307, August.
  10. Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, vol. 88(351), pages 549-63, September.
  11. Blommestein, H.J. & Nijkamp, P., 1983. "Testing the spatial scale and the dynamic structure in regional models : a contribution to spatial econometric specification analysis," Serie Research Memoranda 0016, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  12. Bronars, Stephen G. & Jansen, Dennis W., 1987. "The geographic distribution of unemployment rates in the U.S. : A spatial-time series analysis," Journal of Econometrics, Elsevier, vol. 36(3), pages 251-279, November.
  13. Pace, R Kelley, et al, 1998. "Spatiotemporal Autoregressive Models of Neighborhood Effects," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 15-33, July.
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